AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Overall, both intrapersonal and interpersonal factors predict depressive symptoms among junior high school students, although their predictive importance varies substantially across predictors, and the key predictors of adolescent depression are clarified.
An explainable machine learning framework for predicting student depression risk using non-clinical demographic, academic, lifestyle, and psychosocial factors was developed and explainability analysis identified Suicidal Thoughts, Academic Pressure, and Financial Stress as the most influential predictors.
Despite growing recognition that positive youth development (PYD) depends on the dynamic interaction of individual and ecological resources, existing studies rely on linear models that cannot capture high-dimensional, nonlinear predictor configurations. This study applied machine learning to four-wave longitudinal data...
Introduction Adolescent mental health problems, including depression, anxiety, and stress, are an increasing public health concern, yet the factors associated with different mental health outcomes may vary across domains. This study investigated shared and outcome-specific predictive features of depression, anxiety, pe...
An explainable machine-learning approach to identifying cases of academic burnout among high school students based on their observable learning behaviors is suggested, using a publicly accessible dataset that includes information about 649 adolescents and contains no validated burnout measurement.
Excessive and poorly regulated social media use among university students has been linked to reduced concentration, academic underperformance, and declining digital well-being. However, most predictive approaches to identifying at-risk students rely on opaque, black-box machine learning models that offer little insight...
Background Adolescence represents a critical developmental stage characterized by complex stressors, including academic pressure and interpersonal challenges, which significantly elevate the risk of depressive symptoms. While conventional research predominantly relies on linear methodologies focusing on isolated enviro...
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